



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__snake_case     :List[Any]			       ='Create a default config file for Accelerate with only a few flags set.'






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]="no"			,	lowerCAmelCase__	:	str = default_json_config_file			,	lowerCAmelCase__	:	bool = False				)       ->							str:

							'''simple docstring'''






							A      						=    Path(lowerCAmelCase__				)
							path.parent.mkdir(parents=lowerCAmelCase__			,	exist_ok=lowerCAmelCase__				)
							if path.exists():
														print(
														    F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
														return False
							A      						=    mixed_precision.lower()
							if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
														raise ValueError(
														    F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
							A      						=    {
							    'compute_environment': 'LOCAL_MACHINE',
							    'mixed_precision': mixed_precision,
							}
							if torch.cuda.is_available():
														A      						=    torch.cuda.device_count()
														A      						=    num_gpus
														A      						=    False
														if num_gpus > 1:
																					A      						=    'MULTI_GPU'
														else:
																					A      						=    'NO'
							elif is_xpu_available() and use_xpu:
														A      						=    torch.xpu.device_count()
														A      						=    num_xpus
														A      						=    False
														if num_xpus > 1:
																					A      						=    'MULTI_XPU'
														else:
																					A      						=    'NO'
							elif is_npu_available():
														A      						=    torch.npu.device_count()
														A      						=    num_npus
														A      						=    False
														if num_npus > 1:
																					A      						=    'MULTI_NPU'
														else:
																					A      						=    'NO'
							else:
														A      						=    0
														A      						=    True
														A      						=    1
														A      						=    'NO'
							A      						=    ClusterConfig(**lowerCAmelCase__				)
							config.to_json_file(lowerCAmelCase__				)
							return path






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	Optional[int]				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    parser.add_parser('default'			,	parents=lowerCAmelCase__			,	help=lowerCAmelCase__			,	formatter_class=lowerCAmelCase__				)
							parser.add_argument(
							    '--config_file'			,	default=lowerCAmelCase__			,	help=(
							        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
							        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
							        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
							        'with \'huggingface\'.'
							    )			,	dest='save_location'			,	)

							parser.add_argument(
							    '--mixed_precision'			,	choices=['no', 'fp16', 'bf16']			,	type=lowerCAmelCase__			,	help='Whether or not to use mixed precision training. '
							    'Choose between FP16 and BF16 (bfloat16) training. '
							    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'			,	default='no'			,	)
							parser.set_defaults(func=lowerCAmelCase__				)
							return parser






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]				)       ->							Tuple:

							'''simple docstring'''






							A      						=    write_basic_config(args.mixed_precision			,	args.save_location				)
							if config_file:
														print(F'''accelerate configuration saved at {config_file}'''				)



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE     :					int      =       logging.getLogger()
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							argparse.ArgumentParser()
     parser.add_argument('''-f'''							)
     snake_case											=							parser.parse_args()
     return args.f

class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							logging.StreamHandler(sys.stdout       )
         logger.addHandler(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0     ,   '''run_glue_deebert.py'''       )
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   snake_case											=							run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(__snake_case     ,   0.666       )




    @slow
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )




